Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2023
Research article |
| 13 Feb 2023
| 13 Feb 2023
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Maxim L. Lamare
Department of Earth Sciences, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
John D. Hedley
Numerical Optics Ltd, Witheridge, Tiverton, Devon, EX16 8AA, UK
Department of Earth Sciences, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
Related authors
No articles found.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
Short summary
Short summary
Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Rosalie H. Shepherd, Martin D. King, Amelia A. Marks, Neil Brough, and Andrew D. Ward
Atmos. Chem. Phys., 18, 5235–5252, https://doi.org/10.5194/acp-18-5235-2018, https://doi.org/10.5194/acp-18-5235-2018, 2018
Short summary
Short summary
The refractive index of atmospheric extracts sourced from urban (London), remote (Antarctica), and woodsmoke aerosol was determined by applying optical trapping simultaneously with Mie spectroscopy. In addition, owing to the absorbing nature of woodsmoke and an aqueous humic acid aerosol extract, the absorption Ångström exponent could be determined.The refractive index and absorption Ångström exponent were then applied in a top-of-the-atmosphere albedo radiation transfer model.
Hoi Ga Chan, Markus M. Frey, and Martin D. King
Atmos. Chem. Phys., 18, 1507–1534, https://doi.org/10.5194/acp-18-1507-2018, https://doi.org/10.5194/acp-18-1507-2018, 2018
Short summary
Short summary
Emissions of reactive nitrogen from snowpacks influence remote air quality. Two physical air–snow models for nitrate were developed. One assumes that below a threshold temperature the air–snow grain interface is pure ice and above it a disordered interface emerges. The other assumes an air–ice interface below melting and that any liquid present is concentrated in micropockets. Only the latter matches observations at two Antarctic lcoations covering a wide range of environmental conditions.
Amelia A. Marks, Maxim L. Lamare, and Martin D. King
The Cryosphere, 11, 2867–2881, https://doi.org/10.5194/tc-11-2867-2017, https://doi.org/10.5194/tc-11-2867-2017, 2017
Short summary
Short summary
Arctic sea ice extent is declining rapidly. Prediction of sea ice trends relies on sea ice models that need to be evaluated with real data. A realistic sea ice environment is created in a laboratory by the Royal Holloway sea ice simulator and is used to show a sea ice model can replicate measured properties of sea ice, e.g. reflectance. Black carbon, a component of soot found in atmospheric pollution, is also experimentally shown to reduce the sea ice reflectance, which could exacerbate melting.
M. L. Lamare, J. Lee-Taylor, and M. D. King
Atmos. Chem. Phys., 16, 843–860, https://doi.org/10.5194/acp-16-843-2016, https://doi.org/10.5194/acp-16-843-2016, 2016
Short summary
Short summary
The decrease in reflectivity (albedo) of sea ice and snow containing mineral dusts and volcanic ashes is calculated. The type of snow and sea ice, the thickness and the layering of mineral aerosol deposits are varied. The results show that the response of the albedo of snow and sea ice to mineral aerosol deposits is more sensitive to the type of snow or sea ice than to the properties of the mineral aerosol deposits themselves.
M. M. Frey, H. K. Roscoe, A. Kukui, J. Savarino, J. L. France, M. D. King, M. Legrand, and S. Preunkert
Atmos. Chem. Phys., 15, 7859–7875, https://doi.org/10.5194/acp-15-7859-2015, https://doi.org/10.5194/acp-15-7859-2015, 2015
Short summary
Short summary
Surprisingly large concentrations and flux of atmospheric nitrogen oxides were measured at Dome C, East Antarctica. It was found that the surface snow holds a significant reservoir of photochemically produced NOx and is a sink of gas-phase ozone. Main drivers of NOx snow emissions were large snow nitrate concentrations, with contributions of increased UV from decreases in stratospheric ozone. Observed halogen and hydroxyl radical concentrations were too low to explain large NO2:NO ratios.
H. G. Chan, M. D. King, and M. M. Frey
Atmos. Chem. Phys., 15, 7913–7927, https://doi.org/10.5194/acp-15-7913-2015, https://doi.org/10.5194/acp-15-7913-2015, 2015
S. Preunkert, M. Legrand, M. M. Frey, A. Kukui, J. Savarino, H. Gallée, M. King, B. Jourdain, W. Vicars, and D. Helmig
Atmos. Chem. Phys., 15, 6689–6705, https://doi.org/10.5194/acp-15-6689-2015, https://doi.org/10.5194/acp-15-6689-2015, 2015
Short summary
Short summary
During two austral summers HCHO was investigated in air, snow, and interstitial air at the Concordia site located on the East Antarctic Plateau. Snow emission fluxes were estimated to be around 1 to 2 and 3 to 5 x 10^12 molecules m-2 s-1 at night and at noon, respectively. Shading experiments suggest that the photochemical HCHO production in the snowpack at Concordia remains negligible. The mean HCHO level of 130pptv observed at 1m above the surface is quite well reproduced by 1-D simulations.
A. Kukui, M. Legrand, S. Preunkert, M. M. Frey, R. Loisil, J. Gil Roca, B. Jourdain, M. D. King, J. L. France, and G. Ancellet
Atmos. Chem. Phys., 14, 12373–12392, https://doi.org/10.5194/acp-14-12373-2014, https://doi.org/10.5194/acp-14-12373-2014, 2014
Short summary
Short summary
Concentrations of OH radicals and the sum of peroxy radicals, RO2, were measured in the boundary layer for the first time on the East Antarctic Plateau at the Concordia Station during the austral summer 2011/2012. The concentrations of radicals were comparable to those observed at the South Pole, confirming that the elevated oxidative capacity of the Antarctic atmospheric boundary layer found at the South Pole is not restricted to the South Pole but common over the high Antarctic plateau.
M. Legrand, S. Preunkert, M. Frey, Th. Bartels-Rausch, A. Kukui, M. D. King, J. Savarino, M. Kerbrat, and B. Jourdain
Atmos. Chem. Phys., 14, 9963–9976, https://doi.org/10.5194/acp-14-9963-2014, https://doi.org/10.5194/acp-14-9963-2014, 2014
A. A. Marks and M. D. King
The Cryosphere, 8, 1625–1638, https://doi.org/10.5194/tc-8-1625-2014, https://doi.org/10.5194/tc-8-1625-2014, 2014
Q. Libois, G. Picard, J. L. France, L. Arnaud, M. Dumont, C. M. Carmagnola, and M. D. King
The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, https://doi.org/10.5194/tc-7-1803-2013, 2013
A. A. Marks and M. D. King
The Cryosphere, 7, 1193–1204, https://doi.org/10.5194/tc-7-1193-2013, https://doi.org/10.5194/tc-7-1193-2013, 2013
M. M. Frey, N. Brough, J. L. France, P. S. Anderson, O. Traulle, M. D. King, A. E. Jones, E. W. Wolff, and J. Savarino
Atmos. Chem. Phys., 13, 3045–3062, https://doi.org/10.5194/acp-13-3045-2013, https://doi.org/10.5194/acp-13-3045-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Aerial observations of sea ice breakup by ship waves
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
Inter-comparison and evaluation of Arctic sea ice type products
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Assessment of Arctic Sea Ice Thickness Retrieval Ability of the Chinese HY-2B Radar Altimeter
Observing sea ice flexural-gravity waves with ground-based radar interferometry
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2
Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements
Observations of sea ice melt from Operation IceBridge imagery
Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays
Brief Communication: Mesoscale and submesoscale dynamics in the marginal ice zone from sequential synthetic aperture radar observations
Classification of sea ice types in Sentinel-1 synthetic aperture radar images
A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data
Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions
Opportunistic evaluation of modelled sea ice drift using passively drifting telemetry collars in Hudson Bay, Canada
Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik
Satellite observations of unprecedented phytoplankton blooms in the Maud Rise polynya, Southern Ocean
Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice
Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates
Broadband albedo of Arctic sea ice from MERIS optical data
Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations
Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison
The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
Estimation of turbulent heat flux over leads using satellite thermal images
Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Short summary
By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
Short summary
Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
Short summary
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
Short summary
Short summary
Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
Short summary
Short summary
Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Tao Zeng, and Suhui Wu
EGUsphere, https://doi.org/10.5194/egusphere-2022-870, https://doi.org/10.5194/egusphere-2022-870, 2022
Short summary
Short summary
With the development of satellite altimeters, satellite altimeter technologies have been increasingly used to retrieve Arctic sea ice thicknesses and have achieved rapid application. However, few reports have explored the retrieval of sea ice thickness by Chinese altimeters among recent studies. Therefore, it is of great significance to obtain reliable Arctic sea ice thickness products based on the China HY-2B radar altimeter to provide data support for the study of long-term changes in sea ice.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-128, https://doi.org/10.5194/tc-2022-128, 2022
Revised manuscript accepted for TC
Short summary
Short summary
We investigate the Gamma Portable Radar Interferometer (GPRI) as a tool for evaluating flexural-gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20 to 50-second infragravity waves in landfast ice with ~1 mm amplitude during 23–24 Apr 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers confirming the ability to track propagation and properties of waves over hundreds of meters.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
Short summary
Short summary
Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
Short summary
Short summary
Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Short summary
High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Short summary
This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
Short summary
Short summary
Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
Short summary
Short summary
Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
Short summary
Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
Short summary
Short summary
We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
Short summary
Short summary
We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
Short summary
Short summary
Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
Short summary
Short summary
A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
Short summary
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
Short summary
Short summary
Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Hoyeon Shi, Byung-Ju Sohn, Gorm Dybkjær, Rasmus Tage Tonboe, and Sang-Moo Lee
The Cryosphere, 14, 3761–3783, https://doi.org/10.5194/tc-14-3761-2020, https://doi.org/10.5194/tc-14-3761-2020, 2020
Short summary
Short summary
To estimate sea ice thickness from satellite freeboard measurements, snow depth information has been required; however, the snow depth estimate has been considered largely uncertain. We propose a new method to estimate sea ice thickness and snow depth simultaneously from freeboards by imposing a thermodynamic constraint. Obtained ice thicknesses and snow depths were consistent with airborne measurements, suggesting that uncertainty of ice thickness caused by uncertain snow depth can be reduced.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
Short summary
Short summary
This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Wolfgang Dierking, Harry L. Stern, and Jennifer K. Hutchings
The Cryosphere, 14, 2999–3016, https://doi.org/10.5194/tc-14-2999-2020, https://doi.org/10.5194/tc-14-2999-2020, 2020
Short summary
Short summary
Monitoring deformation of sea ice is useful for studying effects of ice compression and divergent motion on the ice mass balance and ocean–ice–atmosphere interactions. In calculations of deformation parameters not only the measurement uncertainty of drift vectors has to be considered. The size of the area and the time interval used in the calculations have to be chosen within certain limits to make sure that the uncertainties of deformation parameters are smaller than their real magnitudes.
Igor E. Kozlov, Evgeny V. Plotnikov, and Georgy E. Manucharyan
The Cryosphere, 14, 2941–2947, https://doi.org/10.5194/tc-14-2941-2020, https://doi.org/10.5194/tc-14-2941-2020, 2020
Short summary
Short summary
Here we demonstrate a recently emerged opportunity to retrieve high-resolution surface current velocities from sequential spaceborne radar images taken over low-concentration ice regions of polar oceans. Such regularly available data uniquely resolve complex surface ocean dynamics even at small scales and can be used in operational applications to assess and predict transport and distribution of biogeochemical substances and pollutants in ice-covered waters.
Jeong-Won Park, Anton Andreevich Korosov, Mohamed Babiker, Joong-Sun Won, Morten Wergeland Hansen, and Hyun-Cheol Kim
The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, https://doi.org/10.5194/tc-14-2629-2020, 2020
Short summary
Short summary
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.
Marcel König and Natascha Oppelt
The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, https://doi.org/10.5194/tc-14-2567-2020, 2020
Short summary
Short summary
We used data that we collected on RV Polarstern cruise PS106 in summer 2017 to develop a model for the derivation of melt pond depth on Arctic sea ice from reflectance measurements. We simulated reflectances of melt ponds of varying color and water depth and used the sun zenith angle and the slope of the log-scaled reflectance at 710 nm to derive pond depth. We validated the model on the in situ melt pond data and found it to derive pond depth very accurately.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
Short summary
Short summary
Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Ron R. Togunov, Natasha J. Klappstein, Nicholas J. Lunn, Andrew E. Derocher, and Marie Auger-Méthé
The Cryosphere, 14, 1937–1950, https://doi.org/10.5194/tc-14-1937-2020, https://doi.org/10.5194/tc-14-1937-2020, 2020
Short summary
Short summary
Sea ice drift affects important geophysical and biological processes in the Arctic. Using the motion of dropped polar bear GPS collars, our study evaluated the accuracy of a popular satellite-based ice drift model in Hudson Bay. We observed that velocity was underestimated, particularly at higher speeds. Direction was unbiased, but it was less precise at lower speeds. These biases should be accounted for in climate and ecological research relying on accurate/absolute drift velocities.
Sophie Dufour-Beauséjour, Anna Wendleder, Yves Gauthier, Monique Bernier, Jimmy Poulin, Véronique Gilbert, Juupi Tuniq, Amélie Rouleau, and Achim Roth
The Cryosphere, 14, 1595–1609, https://doi.org/10.5194/tc-14-1595-2020, https://doi.org/10.5194/tc-14-1595-2020, 2020
Short summary
Short summary
Inuit have reported greater variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in snow precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we combined satellite images and time-lapse photography to monitor sea ice in the area between 2015 and 2018.
Babula Jena and Anilkumar N. Pillai
The Cryosphere, 14, 1385–1398, https://doi.org/10.5194/tc-14-1385-2020, https://doi.org/10.5194/tc-14-1385-2020, 2020
Short summary
Short summary
Records of multiple ocean color satellite data indicated unprecedented phytoplankton blooms on the Maud Rise with a backdrop of anomalous upper ocean warming and sea ice loss in 2017. The bloom appearance may indicate it as a potential sink of atmospheric CO2 through biological pumping, and it can be a major source of carbon and energy for the regional food web. The reoccurrence of the bloom is important considering the high-nutrient low-chlorophyll conditions of the Southern Ocean.
Maciej Miernecki, Lars Kaleschke, Nina Maaß, Stefan Hendricks, and Sten Schmidl Søbjærg
The Cryosphere, 14, 461–476, https://doi.org/10.5194/tc-14-461-2020, https://doi.org/10.5194/tc-14-461-2020, 2020
Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
Short summary
Short summary
Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
Short summary
Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Short summary
Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, https://doi.org/10.5194/tc-13-1565-2019, 2019
Short summary
Short summary
Can we ignore the contribution of small ice leads when estimating turbulent heat flux? Combining bulk formulae and a fetch-limited model with surface temperature from MODIS and Landsat-8 Thermal Infrared Sensor (TIRS) images, we found small leads account for 25 % of the turbulent heat flux, due to its large total area. Estimated turbulent heat flux is larger from TIRS than that from MODIS with a coarser resolution and larger using a fetch-limited model than that using bulk formulae.
Daniel Price, Iman Soltanzadeh, Wolfgang Rack, and Ethan Dale
The Cryosphere, 13, 1409–1422, https://doi.org/10.5194/tc-13-1409-2019, https://doi.org/10.5194/tc-13-1409-2019, 2019
Short summary
Short summary
Snow depth on Antarctic sea ice is poorly mapped. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound, with a focus on a novel approach using a high-resolution numerical snow accumulation model. We find the model performs better than existing snow products from reanalysis products. However, when combining this information with satellite data to retrieve sea ice thickness, large uncertainties in thickness remain.
Cited articles
Bacour, C., Bréon, F.-M., Gonzalez, L., Price, I., Muller, J.-P., and
Straume, A. G.: Simulating Multi-Directional Narrowband Reflectance of the
Earth's Surface Using ADAM (A Surface Reflectance Database for ESA's Earth
Observation Missions), Remote Sens., 12, 1679–1703, https://doi.org/10.3390/rs12101679, 2020. a
Ball, C. P., Marks, A. A., Green, P. D., MacArthur, A., Maturilli, M., Fox,
N. P., and King, M. D.: Hemispherical-Directional Reflectance (HDRF) of
Windblown Snow-Covered Arctic Tundra at Large Solar Zenith Angles,
IEEE T. Geosci. Remote, 53, 5377–5387,
https://doi.org/10.1109/TGRS.2015.2421733, 2015. a
Becker, S., Ehrlich, A., Jäkel, E., Carlsen, T., Schäfer, M., and Wendisch, M.: Airborne measurements of directional reflectivity over the Arctic marginal sea ice zone, Atmos. Meas. Tech., 15, 2939–2953, https://doi.org/10.5194/amt-15-2939-2022, 2022. a
Beine, H., Anastasio, C., Domine, F., Douglas, T., Barret, M., France, J.,
King, M., Hall, S., and Ullmann, K.: Soluble chromophores in marine snow,
seawater, sea ice and frost flowers near Barrow, Alaska, J. Geophys. Res.-Atmos., 117, D00R15, https://doi.org/10.1029/2011JD016650, 2012. a
Buckley, R. G. and Trodahl, H. J.: Scattering and absorption of visible light
by sea ice, Nature, 326, 867–869, https://doi.org/10.1038/326867a0, 1987. a
Carlsen, T., Birnbaum, G., Ehrlich, A., Helm, V., Jäkel, E., Schäfer, M., and Wendisch, M.: Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica, The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, 2020. a, b
Chang, H. and Charalampopoulos, T. T.: Determination of the Wavelength
Dependence of Refractive Indices of Flame Soot, P. Roy. Soc. Lond. A Mat., 430,
577–591, https://doi.org/10.1098/rspa.1990.0107, 1990. a
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea Ice-Albedo Climate Feedback
Mechanism, J. Climate, 8, 240–247,
https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2, 1995. a
Doherty, S. J., Warren, S. G., Grenfell, T. C., Clarke, A. D., and Brandt, R. E.: Light-absorbing impurities in Arctic snow, Atmos. Chem. Phys., 10, 11647–11680, https://doi.org/10.5194/acp-10-11647-2010, 2010. a
Dumont, M., Brissaud, O., Picard, G., Schmitt, B., Gallet, J.-C., and Arnaud, Y.: High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths – comparison with modelling results, Atmos. Chem. Phys., 10, 2507–2520, https://doi.org/10.5194/acp-10-2507-2010, 2010. a
Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day
climate forcing and response from black carbon in snow, J. Geophys. Res.-Atmos., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.:
Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere
between 1979 and 2008, Nature Geosci., 4, 151–155,
https://doi.org/10.1038/ngeo1062, 2011. a
France, J. L., King, M. D., Lee-Taylor, J., Beine, H. J., Ianniello, A.,
Domine, F., and MacArthur, A.: Calculations of in-snow NO2 and OH radical
photochemical production and photolysis rates: A field and radiative-transfer
study of the optical properties of Arctic (Ny-Ålesund, Svalbard) snow,
J. Geophys. Res.-Earth, 116, F04013, https://doi.org/10.1029/2011JF002019, 2011. a
France, J. L., Reay, H. J., King, M. D., Voisin, D., Jacobi, H. W., Domine, F.,
Beine, H., Anastasio, C., MacArthur, A., and Lee-Taylor, J.: Hydroxyl radical
and NOx production rates, black carbon concentrations and light-absorbing
impurities in snow from field measurements of light penetration and nadir
reflectivity of onshore and offshore coastal Alaskan snow, J. Geophys. Res.-Atmos., 117, D00R12, https://doi.org/10.1029/2011JD016639, 2012. a
Gatebe, C. K. and King, M. D.: Airborne spectral BRDF of various surface
types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers)
for remote sensing applications, Remote Sens. Environ., 179, 131–148, https://doi.org/10.1016/j.rse.2016.03.029, 2016. a, b
Goyens, C., Marty, S., Leymarie, E., Antoine, D., Babin, M., and Bélanger,
S.: High Angular Resolution Measurements of the Anisotropy of Reflectance of
Sea Ice and Snow, Earth and Space Science, 5, 30–47,
https://doi.org/10.1002/2017EA000332, 2018. a
Hedley, J.: A three-dimensional radiative transfer model for shallow water
environments, Opt. Express, 16, 21887–21902, https://doi.org/10.1364/OE.16.021887,
2008. a, b, c
Hedley, J. D., Roelfsema, C. M., Phinn, S. R., and Mumby, P. J.: Environmental
and Sensor Limitations in Optical Remote Sensing of Coral Reefs: Implications
for Monitoring and Sensor Design, Remote Sens., 4, 271,
https://doi.org/10.3390/rs4010271, 2012. a
Henyey, L. G. and Greenstein, J. L.: Diffuse radiation in the Galaxy,
Astrophys. J., 93, 70–83, https://doi.org/10.1086/144246, 1941. a
Hudson, S. R., Kato, S., and Warren, S. G.: Evaluating CERES angular
distribution models for snow using surface reflectance observations from the
East Antarctic Plateau, J. Geophys. Res.-Atmos., 115, D03101, https://doi.org/10.1029/2009JD012624, 2010. a
King, M., France, J., Fisher, F., and Beine, H.: Measurement and modelling of
UV radiation penetration and photolysis rates of nitrate and hydrogen
peroxide in Antarctic sea ice: An estimate of the production rate of hydroxyl
radicals in first-year sea ice, J. Photoch. Photobio. A, 176, 39–49,
https://doi.org/10.1016/j.jphotochem.2005.08.032, 2005. a
Kotchenova, S. Y., Vermote, E. F., Levy, R., and Lyapustin, A.: Radiative
transfer codes for atmospheric correction and aerosol retrieval:
intercomparison study, Appl. Opt., 47, 2215–2226, 2008. a
Lamare, M. L., Lee-Taylor, J., and King, M. D.: The impact of atmospheric mineral aerosol deposition on the albedo of snow & sea ice: are snow and sea ice optical properties more important than mineral aerosol optical properties?, Atmos. Chem. Phys., 16, 843–860, https://doi.org/10.5194/acp-16-843-2016, 2016. a, b, c, d, e, f, g
Lamare, M., Hedley, J., and King, M.: The effects of surface roughness on the spectral (300–1400 nm) bidirectional reflectance distribution function (BRDF) of sea ice, Zenodo [data set], https://doi.org/10.5281/zenodo.5733402, 2021. a
Lee-Taylor, J. and Madronich, S.: Calculation of actinic fluxes with a coupled
atmosphere–snow radiative transfer model, J. Geophys. Res.-Atmos., 107, ACH 22-1–ACH 22-10, https://doi.org/10.1029/2002JD002084, 2002. a
Leroy, M. and Roujean, J. L.: Sun and view angle corrections on reflectances
derived from NOAA/AVHRR data, IEEE T. Geosci. Remote, 32, 684–697, https://doi.org/10.1109/36.297985, 1994. a
Li, S. and Zhou, X.: Modelling and measuring the spectral bidirectional
reflectance factor of snow-covered sea ice: an intercomparison study,
Hydrol. Process., 18, 3559–3581, 2004. a
Li, Z., Cihlar, J., Zheng, X., Moreau, L., and Ly, H.: The bidirectional
effects of AVHRR measurements over boreal regions, IEEE T. Geosi. Remote, 34, 1308–1322, https://doi.org/10.1109/36.544556, 1996. a
Light, B., Maykut, G. A., and Grenfell, T. C.: A temperature-dependent,
structural-optical model of first-year sea ice, J. Geophys. Res.-Oceans, 109, C06013, https://doi.org/10.1029/2003JC002164, 2004. a
Lim, A., Hedley, J. D., LeDrew, E., Mumby, P. J., and Roelfsema, C.: The
effects of ecologically determined spatial complexity on the classification
accuracy of simulated coral reef images, Remote Sens. Environ., 113,
965–978, https://doi.org/10.1016/j.rse.2009.01.011, 2009. a
Malinka, A., Zege, E., Istomina, L., Heygster, G., Spreen, G., Perovich, D., and Polashenski, C.: Reflective properties of melt ponds on sea ice, The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, 2018. a
Manninen, A. T.: Surface roughness of Baltic sea ice, J. Geophys. Res.-Oceans, 102, 1119–1139, https://doi.org/10.1029/96JC02991, 1997. a
Manninen, T., Anttila, K., Jääskeläinen, E., Riihelä, A., Peltoniemi, J., Räisänen, P., Lahtinen, P., Siljamo, N., Thölix, L., Meinander, O., Kontu, A., Suokanerva, H., Pirazzini, R., Suomalainen, J., Hakala, T., Kaasalainen, S., Kaartinen, H., Kukko, A., Hautecoeur, O., and Roujean, J.-L.: Effect of small-scale snow surface roughness on snow albedo and reflectance, The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, 2021. a, b
Marks, A. A. and King, M. D.: The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover, The Cryosphere, 7, 1193–1204, https://doi.org/10.5194/tc-7-1193-2013, 2013. a, b, c
Marks, A. A. and King, M. D.: The effect of snow/sea ice type on the response of albedo and light penetration depth (e-folding depth) to increasing black carbon, The Cryosphere, 8, 1625–1638, https://doi.org/10.5194/tc-8-1625-2014, 2014. a, b, c
Miao, Y., Peng, L., Xiao-wei, C., Ming-guang, T., Qing-kai, W., and Zhi-jun,
L.: Field Observations of the Bidirectional Reflectance Characteristics of
Lake Ice, Spectrosc. Spect. Anal., 40, 2453–2461, 2020. a
Mishchenko, M. I., Dlugach, J. M., Yanovitskij, E. G., and Zakharova, N. T.:
Bidirectional reflectance of flat, optically thick particulate layers: an
efficient radiative transfer solution and applications to snow and soil
surfaces, 18, 3559–3581, J. Quant. Spectrosc. Ra.,
1999. a
Mobley, C. D.: A numerical model for the computation of radiance distributions
in natural waters with wind-roughened surfaces, Limnol. Oceanogr.,
34, 1473–1483, https://doi.org/10.4319/lo.1989.34.8.1473, 1989. a
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal
evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.-Oceans, 107, 8044, https://doi.org/10.1029/2000JC000438, 8044, 2002. a
Peterson, I. K., Prinsenberg, S. J., and Holladay, J. S.: Observations of sea
ice thickness, surface roughness and ice motion in Amundsen Gulf, J. Geophys. Res.-Oceans, 113, C060016, https://doi.org/10.1029/2007JC004456, 2008. a
Qu, X. and Hall, A.: Surface Contribution to Planetary Albedo Variability in
Cryosphere Regions, J. Climate, 18, 5239–5252,
https://doi.org/10.1175/JCLI3555.1, 2005. a
Qu, Y., Liang, S., Liu, Q., He, T., Liu, S., and Li, X.: Mapping Surface
Broadband Albedo from Satellite Observations: A Review of Literatures on
Algorithms and Products, Remote Sens., 7, 990–1020,
https://doi.org/10.3390/rs70100990, 2015. a
Qu, Y., Liang, S., Liu, Q., Li, X., Feng, Y., and Liu, S.: Estimating Arctic
sea-ice shortwave albedo from MODIS data, Remote Sens. Environ., 186,
32–46, https://doi.org/10.1016/j.rse.2016.08.015, 2016a. a
Qu, Y., Liang, S., Liu, Q., Li, X., Feng, Y., and Liu, S.: Estimating Arctic
sea-ice shortwave albedo from MODIS data, Remote Sens. Environ., 186,
32–46, https://doi.org/10.1016/j.rse.2016.08.015, 2016b. a
Redmond Roche, B. H. and King, M. D.: Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo, The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, 2022. a
Schlosser, E.: Optical studies of Antarctic sea ice, Cold Reg. Sci. Technol., 15, 289–293, https://doi.org/10.1016/0165-232X(88)90075-4, 1988. a
Stamnes, K., Hamre, B., Stamnes, J., Ryzhikov, G., Biryulina, M., Mahoney, R.,
Hauss, B., and Sei, A.: Modeling of radiation transport in coupled
atmosphere-snow-ice-ocean systems, J. Quant. Spectrosc. Ra., 112, 714–726, https://doi.org/10.1016/j.jqsrt.2010.06.006, 2011. a, b
Torrance, K. E. and Sparrow, E. M.: Theory for Off-Specular Reflection From
Roughened Surfaces, J. Opt. Soc. Am., 57, 1105–1114,
https://doi.org/10.1364/JOSA.57.001105, 1967.
a
Tucker, W. B., Perovich, D. K., Gow, A. J., Weeks, W. F., and Drinkwater,
M. R.: Physical Properties of Sea Ice Relevant to Remote Sensing, 9–28,
American Geophysical Union, https://doi.org/10.1029/GM068p0009, 2013. a, b
Voisin, D., Jaffrezo, J.-L., Houdier, S., Barret, M., Cozic, J., King, M. D.,
France, J. L., Reay, H. J., Grannas, A., Kos, G., Ariya, P. A., Beine, H. J.,
and Domine, F.: Carbonaceous species and humic like substances (HULIS) in
Arctic snowpack during OASIS field campaign in Barrow, J. Geophys. Res.-Atmos., 117, D00R19, https://doi.org/10.1029/2011JD016612, 2012. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet
to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008. a
Warren, S. G., Brandt, R. E., and O'Rawe Hinton, P.: Effect of surface
roughness on bidirectional reflectance of Antarctic snow, J. Geophys. Res.-Planet., 103, 25789–25807,
https://doi.org/10.1029/98JE01898, 1998. a
Weeks, W. F. and Ackley, S. F.: Growth conditions and the structure and
properties of sea ice, in: Physics of Ice Covered Seas: An Advanced Study
Institute-Summer School, Springer, https://doi.org/10.1007/978-1-4899-5352-0_2, 1994. a
Zege, E., Malinka, A., Katsev, I., Prikhach, A., Heygster, G., Istomina, L.,
Birnbaum, G., and Schwarz, P.: Algorithm to retrieve the melt pond fraction
and the spectral albedo of Arctic summer ice from satellite optical data,
Remote Sens. Environ., 163, 153–164,
https://doi.org/10.1016/j.rse.2015.03.012, 2015. a
Short summary
The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice ...
